The Impact of Electric Cars on Oil Demand and Greenhouse Gas Emissions in Key Markets

This book explores the extent to which electric cars might reduce oil demand and greenhouse gas emissions in key markets: China, France, Germany, India, Japan and the United States. The developed model consists of an econometric sub-model, soft-linked with a system dynamics sub-model. The model captures feedback loops that may stimulate the market development of electric cars. The six countries are interlinked to simulate technological progress concerning the electric vehicle battery

The Impact of Electric Cars on Oil Demand and Greenhouse Gas Emissions in Key Markets

This thesis explores the extent to which electric cars might reduce oil demand and greenhouse gas emissions in key markets: China, France, Germany, India, Japan and the United States. To meet this objective, a dynamic model capable of simulating the market evolution of nine powertrain technologies between 2000 and 2030 is developed. The model consists of an econometric sub-model, soft-linked with a system dynamics sub-model. The purpose of the time-series econometric sub-model is to project country-specific total car stock. To this end, six single-equation regressions based on autoregressive integrated moving average or autoregressive distributed-lag techniques are estimated. The purpose of the system dynamics sub-model is to represent feedback processes and facilitate policy analysis. The effects of six policy measures are examined: emission standards, energy taxation, electric car purchase subsidies, investment in recharging stations, investment in hydrogen refuelling infrastructure and desired car occupancy. The dynamic hypothesis of the model captures feedback loops that may stimulate the market development of electric cars. The six countries are interlinked to simulate technological progress concerning the electric vehicle battery. In particular, its cost, price and capacity, together with the resulting electric range of the car, are investigated. Two scenarios are constructed: under the Alternative Scenario, the market uptake of electric cars is faster due to a favourable policy package. This leads to a decline in oil demand and direct greenhouse gas emissions as well as to an increase in electricity demand from cars compared to the Reference Scenario. The methodological linkage of econometrics and system dynamics, together with the endogenisation of the electric vehicle battery price evolution by explicitly modelling six major car markets, is the main contribution of this study. Its major limitations prompt further research on the representation of supply-side aspects (i.e. battery and vehicle manufacturers) using alternative methods such as agent-based modelling

Analysis and testing of electric car incentive scenarios in the Netherlands and Norway

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Analysis of transport emissions from a global perspective

This article has a double objective; On the one hand, to present the primary data on the evolution and current situation of emissions from the transport sector from a global perspective. On the other hand, to analyse the decisive role that the reduction of emissions in this sector can have in the fight against the climate crisis, despite the future increase in the demand for transport in the world, motivated by the growth of the population and the demand transportation of people and goods expected for the coming decades.Ministerio Español de Ciencia, Innovación y Universidades, Programa "Salvador de Madariaga" 2019. PRX21/00586Departamento de Ingeniería de Diseño y Proyecto

European governments’ electromobility plans: an assessment with a focus on infrastructure targets and vehicle estimates until 2030

Electromobility offers great potentials to the decarbonisation of the transport sector. The purpose of this study is to analyse the development of electromobility in the European Union (EU) and in the United Kingdom (UK) by 2030. The study is based on the objectives provided by the EU Member States and UK in their national implementation reports, as foreseen by the Directive 2014/94/EU on the deployment of alternative fuels infrastructure. As the initial data coverage was not full, in order to produce a complete data set on registered electric vehicles and public recharging points, we estimated missing values with different statistical techniques and critical analysis of the initial data. A set of proposed indicators, namely the share of electric vehicles, the density of publicly accessible recharging points, the electric vehicles and recharging points annual growth rates and the sufficiency index, were averaged at EU27+UK level to depict the envisaged evolution of electromobility in the present decade. The results show that the objectives of the countries’ governments are overall less ambitious than the goals defined in the EU Green Deal for 2025 and in the Sustainable and Smart Mobility Strategy for 2030. Most of the indicators vary significantly in the 2016–2030 period, often revealing an increased divergence between the development of electric vehicles and public recharging points. Two policy implications are derived: (i) the use of a combined set of indicators to assess the governments’ electromobility plans could be pursued, while the ratio of ten electric vehicles per recharging point may no longer be a useful benchmark; and (ii) measures supporting the uptake of recharging infrastructure are still needed to mitigate the divergence with electric vehicles and to meet the ambitious objectives of the EU Green Deal and Sustainable and Smart Mobility Strategy

How to integrate real-world user behavior into models of the market diffusion of alternative fuels in passenger cars - An in-depth comparison of three models for Germany

The future market diffusion of alternative fuels in the passenger car sector is of great interest to both carmakers and policymakers in order to decrease CO$_{2}$ emissions. The decision to buy a car is not totally objective and only partly based on cost. For this reason, those modeling the future market evolution of cars powered by alternative fuels try to include behavioral and non-cost related aspects. This paper analyzes the integration of user behavior into market diffusion models and compares three models that include this aspect. The comparison comprises three parts: first, it compares the modeling approaches, then uses a harmonized data set to model the future market diffusion of alternative fuel vehicles, with and without behavioral aspects. The most important aspects of user behavior included in the models are the use of charging infrastructure, the limited model availability, the consideration of range anxiety as a hampering factor or the willingness-to-pay-more for alternative drivetrains as a supporting factor, as well as a distinction of users\u27 driving distances. User behavior is considered in various ways, but always has a limiting effect on electric vehicle market diffusion. While a model that distinguishes individual users and driving distances stresses the high relevance of this aspect, it is considered less important in models with a more aggregated inclusion of user behavior based on logit functions

Electric Car Purchase Price as a Factor Determining Consumers’ Choice and their Views on Incentives in Europe

The deployment of zero-emission vehicles has the potential to drastically reduce air pollution and greenhouse gas emissions from road transport. The purpose of this study is to provide evidence on, and quantify the factors that influence, the European market for electric and fuel cell car technologies. The paper reports the results of a stated preference survey among 1,248 car owners in France, Germany, Italy, Poland, Spain and the United Kingdom. The variables that influence powertrain choice are quantified in a nested multinomial logit model. We find that the electric car purchase price continues to be a major deterrent to sales in the surveyed countries. The majority of the respondents considered government incentives as fundamental or important for considering an electric car purchase. Because of the differences in the socio-economic characteristics of consumers in each country, the effectiveness of government incentives may vary across Europe

Simulación del crecimiento urbano de la zona metropolitana Tepic-Xalisco, México

The metropolitan area of Tepic-Xalisco (Nayarit, Mexico) has been experienced a fast growth in the last 30 years, generating situations that put the population and the environment at risk, being urgent and necessary to establish new approaches on strategies of urban planning. Understanding the processes of urban growth and simulating possible scenarios have proven to be an essential tool for decision making in the context of spatial planning. The objective of this project was simulating the urban growth the metropolitan area Tepic-Xalisco at the year 2045 horizon. Three different models were used: Multi-Criteria Evaluation Techniques (MCE), Logistic Regression (LR) and Cellular Automata with Markov chains (CA-Markov) to verify the one that better fits the spatial reality and establish a trend situation future. The results were validated with the actual data of urban occupation of 2015. The CA-Markov model showed the best results produced an overall accuracy of 75% and close coincidences in landscape metrics, so this model was used to generate a trend-based scenario of urban growth to the year 2045. The resulting information will be used to generate alternative scenarios that will help to design and evaluate sustainable urban development oriented urban planning strategies.La zona metropolitana Tepic-Xalisco (Nayarit, México) ha tenido un rápido crecimiento en los últimos 30 años, generando situaciones que han puesto en riesgo a la población y medio ambiente, siendo urgente y necesario establecer nuevos enfoques sobre estrategias de planificación urbana. Entender los procesos de crecimiento urbano y simular posibles escenarios futuros han demostrado ser una herramienta esencial para la toma de decisiones en el contexto de la ordenación del territorio. El objetivo del presente trabajo fue simular el crecimiento urbano de la zona metropolitana Tepic-Xalisco al año horizonte 2045. Se utilizaron tres modelos diferentes: técnicas de Evaluación Multi-Criterio (EMC), Regresión Logística (RL) y Autómatas Celulares con cadenas de Markov (AC-Markov), para comprobar el de mejor ajuste a la realidad espacial y establecer una situación tendencial futura. Los resultados fueron validados con datos reales de ocupación urbana del 2015. El modelo AC-Markov mostró mejores resultados al producir una exactitud general del 75 % y coincidencias cercanas en la comparación de las métricas del paisaje, por lo que este modelo fue utilizado para generar un escenario futuro tendencial de crecimiento urbano para el año 2045. La información resultante servirá para generar escenarios alternativos que ayuden a diseñar y evaluar estrategias de planificación urbana orientadas al desarrollo urbano sostenible

Methods for forecasting the market penetration of electric drivetrains in the passenger car market

Current car technologies will not solve upcoming challenges of mitigating greenhouse gas emissions in road transport. Projections of the market penetration by alternative drive train technologies are controversial regarding both forecast market shares and applied scientific methods. Accepting this latter challenge, we provide a (so far missing) overview of methods applied in this field and give some recommendations for further work. Our focus is to classify the applied methods into a convenient pattern and to analyse models from the recent scientific literature which consider the electrification of light-duty vehicles. We differentiate the following bottom-up approaches: Econometric models with disaggregated data (such as discrete choice), and agent-based simulation models. The group of top-down models are subdivided into econometric models with aggregated data (e.g. vehicle stock data), system dynamics, as well as integrated assessment models with general equilibrium models. It becomes obvious that some methods have a stronger methodological background whereas others require comprehensive data sets or can be combined more flexibly with other methods. Even though there is no dominant method, we can identify a trend in the literature towards data-driven hybrid approaches, which considers micro and macro aspects influencing the market penetration of electric vehicles

Soft-linking of a behavioral model for transport with energy system cost optimization applied to hydrogen in EU

Fuel cell electric vehicles (FCEV) currently have the challenge of high CAPEX mainly associated to the fuel cell. This study investigates strategies to promote FCEV deployment and overcome this initial high cost by combining a detailed simulation model of the passenger transport sector with an energy system model. The focus is on an energy system with 95% CO2 reduction by 2050. Soft-linking by taking the powertrain shares by country from the simulation model is preferred because it considers aspects such as car performance, reliability and safety while keeping the cost optimization to evaluate the impact on the rest of the system. This caused a 14% increase in total cost of car ownership compared to the cost before soft-linking. Gas reforming combined with CO2 storage can provide a low-cost hydrogen source for FCEV in the first years of deployment. Once a lower CAPEX for FCEV is achieved, a higher hydrogen cost from electrolysis can be afforded. The policy with the largest impact on FCEV was a purchase subsidy of 5 k€ per vehicle in the 2030–2034 period resulting in 24.3 million FCEV (on top of 67 million without policy) sold up to 2050 with total subsidies of 84 bln€. 5 bln€ of R&D incentives in the 2020–2024 period increased the cumulative sales up to 2050 by 10.5 million FCEV. Combining these two policies with infrastructure and fuel subsidies for 2030–2034 can result in 76 million FCEV on the road by 2050 representing more than 25% of the total car stock. Country specific incentives, split of demand by distance or shift across modes of transport were not included in this study